🦜🕸️LangGraph¶
⚡ Build language agents as graphs ⚡
Overview¶
Suppose you're building a customer support assistant. You want your assistant to be able to:
- Use tools to respond to questions
- Connect with a human if needed
- Be able to pause the process indefinitely and resume whenever the human responds
LangGraph makes this all easy. First install:
Then define your assistant:
import json
from langchain_anthropic import ChatAnthropic
from langchain_community.tools.tavily_search import TavilySearchResults
from langgraph.checkpoint.sqlite import SqliteSaver
from langgraph.graph import END, MessageGraph
from langgraph.prebuilt.tool_node import ToolNode
# Define the function that determines whether to continue or not
def should_continue(messages):
last_message = messages[-1]
# If there is no function call, then we finish
if not last_message.tool_calls:
return END
else:
return "action"
# Define a new graph
workflow = MessageGraph()
tools = [TavilySearchResults(max_results=1)]
model = ChatAnthropic(model="claude-3-haiku-20240307").bind_tools(tools)
workflow.add_node("agent", model)
workflow.add_node("action", ToolNode(tools))
workflow.set_entry_point("agent")
# Conditional agent -> action OR agent -> END
workflow.add_conditional_edges(
"agent",
should_continue,
)
# Always transition `action` -> `agent`
workflow.add_edge("action", "agent")
memory = SqliteSaver.from_conn_string(":memory:") # Here we only save in-memory
# Setting the interrupt means that any time an action is called, the machine will stop
app = workflow.compile(checkpointer=memory, interrupt_before=["action"])
Now, run the graph:
# Run the graph
thread = {"configurable": {"thread_id": "4"}}
for event in app.stream("what is the weather in sf currently", thread, stream_mode="values"):
for v in event.values():
print(v)
action
. The SqliteSaver
persists the state. Resume at any time.
The graph orchestrates everything:
- The
MessageGraph
contains the agent's "Memory" - Conditional edges enable dynamic routing between the chatbot, tools, and the user
- Persistence makes it easy to stop, resume, and even rewind for full control over your application
With LangGraph, you can build complex, stateful agents without getting bogged down in manual state and interrupt management. Just define your nodes, edges, and state schema - and let the graph take care of the rest.
Tutorials¶
Consult the Tutorials to learn more about building with LangGraph, including advanced use cases.
How-To Guides¶
Check out the How-To Guides for instructions on handling common tasks with LangGraph
Reference¶
For documentation on the core APIs, check out the Reference docs.
Conceptual Guides¶
Once you've learned the basics, if you want to further understand LangGraph's core abstractions, check out the Conceptual Guides.
Why LangGraph?¶
LangGraph is framework agnostic (each node is a regular python function). It extends the core Runnable API (shared interface for streaming, async, and batch calls) to make it easy to:
- Seamless state management across multiple turns of conversation or tool usage
- The ability to flexibly route between nodes based on dynamic criteria
- Smooth switching between LLMs and human intervention
- Persistence for long-running, multi-session applications
If you're building a straightforward DAG, Runnables are a great fit. But for more complex, stateful applications with nonlinear flows, LangGraph is the perfect tool for the job.